Unscented Kalman Filter for frequency and amplitude estimation

This paper introduces a new digital signal processing algorithm for frequency and amplitude estimation based on Unscented Kalman Filter (UKF). The results of computer simulated and realistic synthetic data tests are presented. The initial parameters used during the tests were chosen carefully using an established parameter estimation method, the Self Tuning Least Square (STLS). It is concluded that the proposed algorithm is simple, efficient and has low computational demands compare to STLS which makes the UKF a very promising method in next generation of power quality monitoring devices.

[1]  Ganapati Panda,et al.  An extended complex Kalman filter for frequency measurement of distorted signals , 2000, 2000 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.00CH37077).

[2]  Kai-Bor Yu,et al.  Total least squares approach for frequency estimation using linear prediction , 1987, IEEE Trans. Acoust. Speech Signal Process..

[3]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[4]  M.D. Ilic,et al.  Electric power system static state estimation through Kalman filtering and load forecasting , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[5]  Jeffrey K. Uhlmann,et al.  Unscented filtering and nonlinear estimation , 2004, Proceedings of the IEEE.

[6]  Gustavo Valverde,et al.  Unscented kalman filter for power system dynamic state estimation , 2011 .

[7]  A.E. Emanuel,et al.  Summary of IEEE standard 1459: definitions for the measurement of electric power quantities under sinusoidal, nonsinusoidal, balanced, or unbalanced conditions , 2004, IEEE Transactions on Industry Applications.

[8]  Bijaya Ketan Panigrahi,et al.  Adaptive complex unscented Kalman filter for frequency estimation of time-varying signals , 2010 .

[9]  Pradipta Kishore Dash,et al.  Fast Tracking of Power Quality Disturbance Signals Using an Optimized Unscented Filter , 2009, IEEE Transactions on Instrumentation and Measurement.

[10]  Vladimir Terzija,et al.  Voltage phasor and local system frequency estimation using Newton type algorithm , 1994 .

[11]  Sanjib Kumar Panda,et al.  Fast estimation of voltage and current phasors in power networks using an adaptive neural network , 1997 .

[12]  Marjan Popov,et al.  Digital Metering of Power Components According to IEEE Standard 1459-2000 Using the Newton-Type Algorithm , 2007, IEEE Transactions on Instrumentation and Measurement.

[13]  Jian Zhang,et al.  Estimation of power quality using an unscented Kalman filter , 2007, TENCON 2007 - 2007 IEEE Region 10 Conference.

[14]  V. Stanojevic,et al.  STLS Algorithm for Power-Quality Indices Estimation , 2008, IEEE Transactions on Power Delivery.

[15]  R. Chudamani,et al.  Real-Time Estimation of Power System Frequency Using Nonlinear Least Squares , 2009, IEEE Transactions on Power Delivery.

[16]  Milenko B. Djurić,et al.  A new self-tuning algorithm for the frequency estimation of distorted signals , 1995 .

[17]  H. Wayne Beaty,et al.  Electrical Power Systems Quality , 1995 .

[18]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[19]  Newton G. Bretas,et al.  An iterative dynamic state estimation and bad data processing , 1989 .

[20]  Barbara F. La Scala,et al.  Design of an extended Kalman filter frequency tracker , 1996, IEEE Trans. Signal Process..